Hümpfer, N. | bioRxiv Science

bioRxiv2024

Septin-associated PIPKIγ splice variants drive centralspindlin association with the midbody via PI(4,5)P2

Mammalian cytokinesis critically depends on the phospholipid phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2] which serves as docking site for crucial components of the cytokinetic machinery at the plasma membrane. PI(4,5)P2 supports several stages of cytokinesis, including actomyosin ring assembly and constriction, membrane tethering of spindle microtubules, and midbody organization. How these various activities of PI(4,5)P2 and the underlying mechanisms of local PI(4,5)P2 synthesis are orchestrated in space and time has remained elusive. Here, we identify a pivotal role of septin-binding splice variants of PIPKI{gamma} that couple nanoscale PI(4,5)P2 synthesis at the ingressing cleavage furrow to late midbody formation. Depletion of PIPKI{gamma} isoforms causes multinucleation and perturbs anillin and septin deposition at the intercellular bridge and at the midbody. These defects are rescued by wild-type kinase, but not by septin binding-deficient or catalytically inactive PIPKI{gamma} variants. We further show that both, septins and PIPKI{gamma} form a complex with centralspindlin, and thereby facilitate the recruitment of centralspindlin to the midbody. Taken together, our findings establish septin-associated PIPKI{gamma} isoforms as key regulators of midbody organization that act through generating a local pool of PI4,5P2 required for centralspindlin recruitment and maintenance at the midbody, and for septin association with microtubules.

cell biology↗

bioRxiv2021

Precise measurement of nanoscopic septin ring structures in deep learning-assisted quantitative superresolution microscopy

The combination of image analysis and fluorescence superresolution microscopy methods allows for unprecedented insight into the organization of macromolecular assemblies in cells. Advances in deep learning-based object recognition enables the automated processing of large amounts of data, resulting in high accuracy through averaging. However, while the analysis of highly symmetric structures of constant size allows for a resolution approaching the dimensions of structural biology, deep learning methods are prone to different forms of bias. A biased recognition of structures may prohibit the development of readouts for processes that involve significant changes in size or shape of amorphous macromolecular complexes. What is required to overcome this problem is a detailed investigation of potential sources of bias and the rigorous testing of trained models using real or simulated data covering a wide dynamic range of possible results. Here we combine single molecule localization-based superresolution microscopy of septin ring structures with the training of several different deep learning models for a quantitative investigation of bias resulting from different training approaches and finally quantitative changes in septin ring structures. We find that trade-off exists between measurement accuracy and the dynamic range of recognized phenotypes. Using our trained models, we furthermore find that septin ring size can be explained by the number of subunits they are assembled from alone. Our work provides a new experimental system for the investigation of septin polymerization.

cell biology↗